Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 11415 publications
Learning Conditional Averages
Marco Bressan
Nataly Brukhim
Nicolo Cesa-Bianchi
Emmanuel Esposito
Shay Moran
Maximilian Thiessen
COLT (2026)
Preview abstract
We introduce the problem of learning \emph{conditional averages} in the PAC framework.
The learner receives a sample labeled by an unknown target concept from a known concept class, as in standard PAC learning. However, instead of learning the target concept itself, the goal is to predict, for each instance, the average label over its \emph{neighborhood}---an arbitrary subset of points that contains the instance.
In the degenerate case where all neighborhoods are singletons, the problem reduces exactly to classic PAC learning. More generally, it extends PAC learning to a setting that captures learning tasks arising in several domains, including explainability, fairness, and recommendation systems. %including explainability, fairness, and recommendation systems.
Our main contribution is a complete characterization of when conditional averages are learnable, together with sample complexity bounds that are tight up to logarithmic factors. The characterization hinges on the joint finiteness of two novel combinatorial parameters, which depend on both the concept class and the neighborhood system, and are closely related to the independence number of the associated neighborhood graph.
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The Synthetic Gap: Automating Forensic Investigation of "AI Slop" with the Scaled Abuse Forensics Examiner (SAFE)
Vahid Jalali
Longling Wang
Geethik Narayana Kamineni
Utkarsh Chaudhary
Crystal Zhao
Lucas Liu
2026
Preview abstract
Generative AI capabilities have enabled malicious actors to flood online platforms with "AI slop"—mass-produced, low-quality synthetic media designed to overwhelm traditional integrity systems. These adversarial campaigns often utilize coordinated networks to distribute unique, localized variations of synthetic content, rendering static detection methods ineffective. The signals to detect coordination often have recall gaps. The content is not exactly duplicative to be in the same repetitive video cluster. The abusers however show similar patterns of behavior which need forensics. Manual forensic investigations cannot scale to match the velocity of these generative attacks.
To address this, we present SAFE (Scaled Abuse Forensics Examiner), an automated multi-agent architecture designed for the scalable forensics of adversarial synthetic media. The system decomposes the investigation process into specialized agents: a Cluster Understanding Agent specialized in analyzing the relations between channels in a cluster, a Behavior Understanding Agent that identifies inorganic spatiotemporal patterns, and a Content Understanding Agent that utilizes LoRA-adapted Large Language Models (LLMs) and few-shot learning to detect existing policy violations and spirit of the policy violations respectively . A Root Agent synthesizes these multimodal signals to render a final verdict. Early deployment results indicate that SAFE significantly accelerates the identification of novel synthetic threats, reducing forensic investigation time compared to human-in-the-loop workflows.
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JAXBench: Benchmarking Autonomous TPU Kernel Optimization
Vijay Janapa Reddi
Charles Hong
Arya Tschand
Julian Walker
Suvinay Subramanian
Shangkun Wang
Sethu Sankaran
Nina Cai
2026
Preview abstract
Evaluation benchmarks have driven progress in automated kernel optimization, yet existing suites target GPUs exclusively. We present JAXBench, a TPU-native benchmark for AI-generated kernel optimization on Google Cloud TPUs. JAXBench comprises 50 JAX workloads, including 17 production LLM operators extracted from architectures in the public MaxText library such as Llama-3.1, DeepSeek-V3, Mixtral, Mamba-2, and AlphaFold2, and 33 fused operator sequences adapted from KernelBench. Eight of the 17 production operators ship with hand-optimized Pallas TPU kernels from the public Tokamax library, whose block sizes we tune via grid search, establishing strong reference baselines. We evaluate one-shot generation, iterative coding agents, the same iterative loop with TPU documentation injected, and a TPU-enabled Autocomp configuration augmented with the same TPU-specific documentation. Across the full 50-benchmark suite with Gemini 3 Flash, best-of-N solves 13/50 benchmarks at 1.01x geomean and iterative refinement reaches 32/50 at 1.18x. Injecting TPU documentation lifts iterative refinement to 48/50 at 1.28x and raises per-sample correctness from 5.8% to 37.3%. Autocomp solves 45/50 but converts those correct kernels into 1.36x geomean with 76% of benchmarks beating XLA. On the 8 hand-tuned references, Autocomp reaches 1.60x against the XLA baseline, showing strong performance against the 2.08x Tokamax geomean but trailing on the specialized paged and ragged attention operators. A Gemini 3.1 Pro ablation lifts Autocomp to 49.1% per-sample correctness and a 3.13x geomean. High-quality TPU kernel generation remains open, and we release the benchmark, profiling harness, and baseline results to support reproducible research.
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The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
JJ Geewax
David R Karger
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26), ACM, Barcelona, Spain, TBD
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Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals(AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly “perfect.” We characterize this as a “Perfection Paradox”—where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer’s role from the “drafter” of specifications to the “curator” of AI-generated patterns.
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Here’s a thought experiment. Say I wave a magic wand across a codebase and an entire class of technical debt, poof, goes away and immediately evaporates if introduced in the future. For example, maybe I make it so that dead feature flags are simply no longer a problem: they just delete themselves as soon as the engineer wills it. Or maybe large-scale migrations just migrate themselves. Maybe we magically have 100% test coverage, without an engineer lifting a finger.
What will happen to developer productivity?
Surely, developer productivity increases overall.
But will the productivity metrics that we all use as a proxy for “developer productivity” move up and to the right. Let’s explore this idea.
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Visual Planning: Let’s Think Only with Images
Han Zhou
Caiqi Zhang
Anna Korhonen
Chengzu Li
Yi Xu
Ivan Vulic
International Conference on Learning Representations (ICLR) (2026)
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Recent advancements in Large Language Models (LLMs) and their multimodal extensions (MLLMs) have significantly enhanced machine reasoning across diverse tasks. However, these models predominantly rely on language as the medium for both expressing and structuring reasoning, even when visual information is present. In this work, we argue that language may not always be the most natural or effective modality for reasoning, particularly in tasks involving spatial, geometric, or physical dynamics. Motivated by this, we propose a new paradigm, Visual Planning, which enables planning through purely visual representations, independent of textual mediation. In this paradigm, planning is executed via sequences of images that encode step-by-step inference in the visual domain, akin to how humans sketch or visualize future actions. We then introduce a novel two-stage reinforcement learning framework empowered by GRPO for post-training large vision models, resulting in substantial improvements in planning accuracy and generalization across both seen and novel scenarios, validated in representative visual navigation tasks, FrozenLake and Maze. Our results establish Visual Planning as a viable and promising alternative to language-based reasoning, opening new avenues for tasks that benefit from intuitive, image-based inference.
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Semantic data models express high-level business concepts and metrics, capturing the business logic needed to query a database correctly. Most data modeling solutions are built as layers above SQL query engines, with bespoke query languages or APIs. The layered approach means that semantic models can’t be used directly in SQL queries. This paper focuses on an open problem in this space – can we define semantic models in SQL, and make them naturally queryable in SQL?
In parallel, graph query is becoming increasingly popular, including in SQL. SQL/PGQ extends SQL with an embedded subset of the GQL graph query language, adding property graph views and making graph traversal queries easy.
We explore a surprising connection: semantic data models are graphs, and defining graphs is a data modeling problem. In both domains, users start by defining a graph model, and need query language support to easily traverse edges in the graph, which means doing joins in the underlying data.
We propose some useful SQL extensions that make it easier to use higher-level data model abstractions in queries. Users can define a “semantic data graph” view of their data, encapsulating the complex business logic required to query the underlying tables correctly. Then they can query that semantic graph model easily with SQL.
Our SQL extensions are useful independently, simplifying many queries – particularly, queries with joins. We make declared foreign key relationships usable for joins at query time – a feature that seems obvious but is notably missing in standard SQL.
In combination, these extensions provide a practical approach to extend SQL incrementally, bringing semantic modeling and graph query together with the relational model and SQL.
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Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation
Yunzhe Qi
Yikun Ban
Allan Stewart
Chuanwei Ruan
Jiachuan He
Shishir Kumar Prasad
Haixun Wang
Jingrui He
Transactions on Machine Learning Research (2026)
Preview abstract
Contextual bandit algorithms aim to identify the optimal choice among a set of candidate arms, based on their contextual information. Among others, the neural contextual bandit algorithms have demonstrated generally superior performance compared to traditional linear and kernel-based methods. Nevertheless, neural methods are not inherently suitable to handle a large number of candidate arms due to their high computational cost when performing neural exploration.
Motivated by the widespread availability of arm category information (e.g., movie genres, retailer types), we formulate contextual bandits into a bi-level recommendation problem based on the accessible arm category information, and propose a novel neural bandit framework, named H2N-Bandit, which utilizes a bi-level hierarchical neural structure to mitigate the substantial computational cost found in conventional neural bandit methods.
To demonstrate its effectiveness, we provide the regret bound for H2N-Bandit under the over-parameterized neural bandit settings. Furthermore, to illustrate its efficiency, we conduct extensive experiments on multiple real-world public data sets with various specifications, showing that H2N-Bandit can significantly reduce the computational cost over existing non-linear methods while achieving better or comparable performances against state-of-the-art baselines.
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"You Have Been Selected as the Winner": Characterizing User-Reported Scams on TikTok
Smirity Kaushik
Kyle Beadle
Gauri Nayak
Madelyn Rose Sanfilippo
Mainack Mondal
Yang Wang
JingJei Li
Yixin Zou
USENIX Symposium on Usable Privacy and Security (SOUPS) (2026)
Preview abstract
Short-form video platforms (SVPs) such as TikTok have grown rapidly in popularity. While online scams have been extensively studied, the extent to which they take new forms on SVPs and the discourses around them remain understudied. Using TikTok as a case study, we analyzed 150 videos in which content creators reported scam experiences and offered anti-scam advice. We focus on how TikTok users (creators, followers, and commenters) discuss scams, rather than analyzing scams. Our analysis surfaces six types of scams, including creator impersonation and account badge verification scams that target TikTok's influencer-follower ecosystem. Scammers also exploit platform-specific features (e.g., direct messaging or the "For You Page") to lure victims. In response, TikTok users share strategies to identify scammer profiles and communication cues, building community support through anti-scam advice. Based on our findings, we offer recommendations for systemizing platform support to combat scams and leveraging the influencer ecosystem to raise awareness.
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Standard evaluations of backdoor attacks on text-to-image (T2I) models primarily measure trigger activation and visual fidelity. We challenge this paradigm, demonstrating that encoder-side poisoning induces persistent, trigger-free semantic corruption that fundamentally reshapes the representation manifold. We trace this vulnerability to a geometric mechanism: a Jacobian-based analysis reveals that backdoors act as low-rank, target-centered deformations that amplify local sensitivity, causing distortion to propagate coherently across semantic neighborhoods. To rigorously quantify this structural degradation, we introduce SEMAD (Semantic Alignment and Drift), a diagnostic framework that measures both internal embedding drift and downstream functional misalignment. Our findings, validated across diffusion and contrastive paradigms, expose the deep structural risks of encoder poisoning and highlight the necessity of geometric audits beyond simple attack success rates.
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Large Language Models utilizing reasoning techniques improve task performance but incur significant latency and token costs due to verbose generation. Existing automatic prompt optimization(APO) frameworks target task accuracy exclusively at the expense of generating long reasoning traces. We propose Cost-Regularized Optimization of Prompts (CROP), an APO method that introduces regularization on response length by generating textual feedback in addition to standard accuracy feedback. This forces the optimization process to produce prompts that elicit concise responses containing only critical information and reasoning. We evaluate our approach on complex reasoning datasets, specifically GSM8K, LogiQA and BIG-Bench Hard. We achieved an 80.6% reduction in token consumption while maintaining competitive accuracy, seeing only a nominal decline in performance. This presents a pragmatic solution for deploying token-efficient and cost-effective agentic AI systems in production pipelines.
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Automating AI research differs from general software engineering due to computationally expensive evaluation (e.g., model training) and opaque performance attribution. Current LLM-based agents struggle here, often generating monolithic scripts that ignore execution costs and causal factors. We introduce MARS (Modular Agent with Reflective Search), a framework optimized for autonomous AI research. MARS relies on three pillars: (1) Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to explicitly balance performance with execution expense; (2) Modular Construction, employing a "Design-Decompose-Implement" pipeline to manage complex research repositories; and (3) Comparative Reflective Memory, which addresses credit assignment by analyzing solution differences to distill high-signal insights. MARS achieves state-of-the-art performance among open-source frameworks on MLE-Bench under comparable settings, maintaining competitiveness with the global leaderboard's top methods. Furthermore, the system exhibits qualitative "Aha!" moments, where 63% of all utilized lessons originate from cross-branch transfer, demonstrating that the agent effectively generalizes insights across search paths.
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CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors
Juro Gottweis
CJ Park
Salman Rahman
Ahmed Metwally
Hong Yu
Ivor Rendulic
Yuzhe Yang
Petar Sirkovic
Daniel McDuff
Shwetak Patel
Nicolas Stroppa
Yubin Kim
Mark Malhotra
Orson Xu
Sam Schmidgall
Tim Althoff
Elahe Vedadi
Cynthia Breazeal
Hae Won Park
(2026)
Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents
Pengfei He
Ash Fox
Stefan Friedli
Daniel Fabian
Burak Gokturk
Jiliang Tang
ICML 2026
Preview abstract
Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Code-RedTeam, a security-aware multi-agent framework designed to mirror real-world red-teaming workflows by integrating security-domain knowledge, code-aware analysis, execution-grounded iterative reasoning, and long-term memory. Code-RedTeam decomposes vulnerability analysis into coordinated discovery and exploitation stages, enabling agents to plan, execute, validate, and refine actions based on real execution feedback while learning from prior trajectories. Extensive evaluations on challenging security benchmarks demonstrate that Code-RedTeam consistently outperforms strong baselines across diverse backbone models, achieving over 60% attack success rate in vulnerability exploitation and up to 10% absolute improvement in vulnerability detection. Ablation and iteration studies further confirm the critical role of execution feedback, structured interaction, and memory for building robust and generalizable cybersecurity agents.
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Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI
Xinyue Gui
Ding Xia
Mark Colley
Yuan Li
Vishal Chauhan
Anubhav Anubhav
Ehsan Javanmardi
Stela Hanbyeol Seo
Chia-Ming Chang
Manabu Tsukada
Takeo Igarashi
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI 26)
Preview abstract
Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.
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